Papers
2
Total Citations
20
H-Index
2
About
Jile Shi is a rising researcher at the intersection of artificial intelligence and minimally invasive surgery, with a primary focus on surgical tool recognition and localization. Their work leverages deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the intelligence of robotic and endoscopic surgical systems. Shi’s most impactful contribution, "Application and evaluation of surgical tool and tool tip recognition based on Convolutional Neural Network in multiple endoscopic surgical scenarios" (2023), has already garnered 18 citations, demonstrating its significance in advancing automated surgical scene understanding. This study systematically evaluated CNN-based tool tip detection across diverse endoscopic environments, laying critical groundwork for real-time surgical assistance. Building on this, their 2025 work on "Development and validation of a surgical tool recognition and localization strategy in robotic surgeries" further refines these techniques for robotic platforms, addressing the unique challenges of tool tracking in dynamic operative fields. Though early in their career, Shi’s research is poised to impact surgical training, workflow optimization, and autonomous robotic assistance. By bridging computer vision and clinical application, they are contributing to a future where AI seamlessly supports surgeons, improving precision and patient outcomes in complex procedures.
Research Focus
Key Achievements
Top Papers
- 1
- 2